python-constraint | Constraint Solving Problem resolver for Python
kandi X-RAY | python-constraint Summary
kandi X-RAY | python-constraint Summary
Constraint Solving Problem resolver for Python
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of python-constraint
python-constraint Key Features
python-constraint Examples and Code Snippets
import constraint
problem = constraint.Problem()
problem.addVariable('x', range(2))
problem.addVariable('y', [1])
def our_constraint(x, y):
if 2*x + 4*y == 6:
return True
problem.addConstraint(our_constraint, ['x','y'])
so
# https://pypi.org/project/python-constraint/
from constraint import *
problem = Problem()
problem.addVariables(["a","b"],range(1,100000))
problem.addConstraint(ExactSumConstraint(5,[4,1]))
problem.getSolutions()
problem.addVariable(variables[15],[4])
problem.addVariable(variables[12], [1])
new_variables = variables[11:] + variables[13:14] + variables[:16]
problem.addVariables(new_variables,domain)
numbers = [11, 16, 18, 19, 61, 66, 68, 69, 81, 86, 88, 89, 91, 96, 98, 99]
target = 264
from constraint import *
problem = Problem()
problem.addVariables(range(16), numbers)
for i in range(4):
# column i
v = [ i + 4*j for j in r
import kiwisolver
x1 = kiwisolver.Variable('x1')
x2 = kiwisolver.Variable('x2')
x3 = kiwisolver.Variable('x3')
x4 = kiwisolver.Variable('x4')
constraints = [1995 <= 2*x1 + 4*x2 + 3*x3 + x4,
2*x1 + 4*x2 + 3*x3 + x4 <=
flight_names = ['MI428', 'UL867', 'QR664', 'TK730', 'UL303']
flight_times = ['T-' + name for name in flight_names]
bays_list = ['A1', 'A2', 'B1', 'B2', 'C1']
times = range(20) # or how many times you have
problem.addVariables(flight_na
from constraint import *
D = 7 # number of devices
L = 3 # number of locations
maxdev = [3,4,2]
allowed = [[1,3,7],[1,3,4,5,6,7],[2,4,5,6]]
problem = Problem()
problem.addVariables(["x_L%d_d%d" %(loc+1,d+1) for loc in range(L) for d in
from constraint import Problem, ExactSumConstraint
problem = Problem()
MaximumSum = 10
MaxMultiplicationSum = 40
Variables = ("a", "b", "c", "d")
realKeys = {'a': 2, 'b': 3, 'c': 4, 'd': 5}
for Variable in Variables:
problem.addVariabl
class product_template(models.Model):
_inherit = 'product.template'
@api.multi
@api.constrains('seller_ids')
def onchange_seller(self):
for record in self :
if not record.seller_ids :
ra
from itertools import groupby
a = [10, 11, 12, 13, 14]
b = [15, 16, 17, 18, 19]
c = [20, 21, 22, 23, 24]
data = [a, b, c]
def keyfunc(t):
return sum(t) > 50
for k, g in groupby(zip(*data), keyfunc):
if k:
g = list(g)
Community Discussions
Trending Discussions on python-constraint
QUESTION
I have a problem where I have 4 variables x1, x2, x3 and x4
. I need to find the values for x1, x2, x3, x4
with the following conditions:
ANSWER
Answered 2019-May-09 at 14:54Your problem is, in fact, linear, and so it's ideally suited to a linear programming approach. However, you are giving it to the solver with no clues as to the linearity of the problem, so it's bound to find that tricky: it pretty much has to try every possibility which is going to take a long time. It might be possible to rewrite your constraints into different forms for the python-constraint
solver (it has, for example, a MaxSumConstraint
constraint form) which might work better but ideally I think you should be using a solver specialised for linear problems.
There is a solver called kiwisolver
which will do what you want. Here's your example converted for that library:
QUESTION
I have a constraint problem that I'm trying to solve with python-constraint
So let's say I have 3 locations: loc1,...loc3
Also, I have 7 devices: device1,...device7
Max amount of devices in each location: loc1:3, loc2:4, loc3:2
(for example maximum of 3 devices in loc1
and so on...)
And some constraints about the locations and the devices:
loc1: device1, device3, device7
,
loc2: device1, device3, device4, device5, device6, device7
loc3: device2, device4, device5, device6
(meaning for example only device1
, device3
and device7
can be in loc1
.)
I'm trying to get a set of possible options for devices in locations.
...ANSWER
Answered 2018-Apr-06 at 13:09This is simple assignment-like model:
So we have a binary variable indicating if device d is assigned to location L. The linear constraints are just:
- assign each device to one location
- each location has a maximum number of devices
- make sure to use only allowed assignments (modeled above by
allowed(L,d)
)
This problem can be handled by any constraint solver.
Enumerating all possible solutions is a bit dangerous. For large instances there are just way too many. Even for this small problem we already have 25 solutions:
For large problems this number will be astronomically large.
Using the Python constraint package this can look like:
QUESTION
I have data like this.
...ANSWER
Answered 2017-Aug-10 at 20:18I suggest you investigate itertools.groupby
. Eg, here's some code that will find & count runs of consecutive columns that sum to >50.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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